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Cheminformatics Crowd Computing for Tuberculosis Drug Discovery

Cheminformatics Crowd Computing for Tuberculosis Drug Discovery (3C4TB) is an innovative crowd-computing initiative to involve individuals and computer algorithms to help prioritize potential leads to accelerate drug discovery for Tuberculosis.

Why Crowd Computing ?

Creation and implementation of methods for prioritization of leads require enormous human intellectual inputs and efforts to realize. Our effort is to provide the basic molecular Kernel or the molecular data-set of active anti-tubercular molecules in standard inter-operable formats and integrate prioritization methodologies to arrive at a small subset of molecules for early-stage trials.

Is the data Open Access / Open Source ?

Yes, all data coming out of this project would be Open Access under CC-BY-SA 2.0. We generally would require the methodologies of all methods used for prioritization be published before-hand in a peer-reviewed journal before inclusion in the data-set archive.

Participating with your method

Participating in this programme with your method is straight forward. You need to download the compendium file in requisite format and use your prioritization method on the data-set. You would require to annotate the output and share the results on the shared repository.

Collaborating on this programmeWe invite and are open to academic collaboration from individuals, institutes or organisations on this initiative. If you have an interesting methodology you would like to try on this data-set, or would like to suggest a methodology which is already not used on this data-set, please contact Dr. Vinod Scaria for more details.

A compendium of data-sets for prioritization and the data-sets filtered through multiple prioritization approaches are available for download.

Molecules with anti-tubercular activities
0k - May 17, 2013, 3:23 AM by Vinod Scaria (v1)
‎Compendium of small molecules with anti-tubercular activities. The molecules are categorised based on the source. Molecular information is available in SMILES, MOL2 and SDF formats‎